information technology — use of biometrics in video

Information technology — Use of biometrics in video surveillance systems —Part 1: System design and specificationTechnologies de l'information — Utilisation de la biométrie dans les systèmes de vidéosurveillance —Partie 1: Conception et spécification

INTERNATIONAL STANDARD

ISO/IEC30137-1

Reference numberISO/IEC 30137-1:2019(E)

First edition2019-05

© ISO/IEC 2019

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Foreword ..........................................................................................................................................................................................................................................vIntroduction ................................................................................................................................................................................................................................vi1 Scope ................................................................................................................................................................................................................................. 12 Normative references ...................................................................................................................................................................................... 23 Terms and definitions ..................................................................................................................................................................................... 2

3.1 Target subject related terms ....................................................................................................................................................... 23.2 VSS related terms .................................................................................................................................................................................. 23.3 Biometric system related terms ............................................................................................................................................... 43.4 Environment/scenario related terms ................................................................................................................................. 43.5 Symbols and abbreviated terms............................................................................................................................................... 5

4 Comparison of terms used in biometric systems with those used in video surveillance ......... 55 Architecture ............................................................................................................................................................................................................... 66 Use cases ....................................................................................................................................................................................................................... 7

6.1 General ........................................................................................................................................................................................................... 76.2 Post event use cases ........................................................................................................................................................................... 86.3 Real time use cases .............................................................................................................................................................................. 86.4 Enrolment use cases ........................................................................................................................................................................... 9

7 Specification of hardware and software ...................................................................................................................................... 97.1 General ........................................................................................................................................................................................................... 97.2 Physical environment ........................................................................................................................................................................ 97.3 Illumination environment .......................................................................................................................................................... 107.4 Inducing frontal view ..................................................................................................................................................................... 107.5 Cameras and supporting infrastructure ......................................................................................................................... 10

7.5.1 Selection of cameras ..................................................................................................................................................107.5.2 Positioning of cameras .............................................................................................................................................117.5.3 Infrastructure considerations ........................................................................................................................... 16

7.6 Biometric software ........................................................................................................................................................................... 177.6.1 General................................................................................................................................................................................... 177.6.2 Face detection software .......................................................................................................................................... 177.6.3 Face comparison software .................................................................................................................................... 187.6.4 Algorithm selection and testing ....................................................................................................................... 187.6.5 Other (non-biometric) software ...................................................................................................................... 18

7.7 Computational requirements .................................................................................................................................................. 187.7.1 General................................................................................................................................................................................... 187.7.2 Core biometric processes ...................................................................................................................................... 197.7.3 Reducing computational expense .................................................................................................................. 20

7.8 Specification for reference image database ................................................................................................................ 207.8.1 General................................................................................................................................................................................... 207.8.2 Reference database size .......................................................................................................................................... 207.8.3 Reference image quality ......................................................................................................................................... 217.8.4 Reference database maintenance ................................................................................................................... 21

8 Multiple camera operation .....................................................................................................................................................................229 Interfaces to related software ..............................................................................................................................................................2210 Guidance for operator assistance ....................................................................................................................................................2311 System design considerations .............................................................................................................................................................23

11.1 General ........................................................................................................................................................................................................ 2311.2 Establishing the business requirements ........................................................................................................................ 2411.3 Site survey ................................................................................................................................................................................................ 2411.4 Size and content of the watchlist .......................................................................................................................................... 2511.5 Performance requirements ....................................................................................................................................................... 26

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11.5.1 General................................................................................................................................................................................... 2611.5.2 Key metrics of performance ................................................................................................................................ 2611.5.3 Presentation Attack Detection (PAD) performance metrics ...................................................27

11.6 Image data and metadata considerations ..................................................................................................................... 27Annex A (informative) Other related (but non-biometric) video analytic techniques and

applications ............................................................................................................................................................................................................28Annex B (informative) Societal considerations and governance processes .............................................................31Annex C (informative) Case study: The use of AFR with VSS for traveller triaging at the border ......33Annex D (informative) Video acquisition measurements ...........................................................................................................35Bibliography .............................................................................................................................................................................................................................45

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Foreword

ISO (the International Organization for Standardization) and IEC (the International Electrotechnical Commission) form the specialized system for worldwide standardization. National bodies that are members of ISO or IEC participate in the development of International Standards through technical committees established by the respective organization to deal with particular fields of technical activity. ISO and IEC technical committees collaborate in fields of mutual interest. Other international organizations, governmental and non-governmental, in liaison with ISO and IEC, also take part in the work.

The procedures used to develop this document and those intended for its further maintenance are described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types of document should be noted. This document was drafted in accordance with the editorial rules of the ISO/IEC Directives, Part 2 (see www .iso .org/directives).

Attention is drawn to the possibility that some of the elements of this document may be the subject of patent rights. ISO and IEC shall not be held responsible for identifying any or all such patent rights. Details of any patent rights identified during the development of the document will be in the Introduction and/or on the ISO list of patent declarations received (see www .iso .org/patents) or the IEC list of patent declarations received (see http: //patents .iec .ch).

Any trade name used in this document is information given for the convenience of users and does not constitute an endorsement.

For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions related to conformity assessment, as well as information about ISO's adherence to the World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT) see www .iso .org/iso/foreword .html.

This document was prepared by Technical Committee ISO/IEC JTC 1, Information technology, Subcommittee SC 37, Biometrics.

A list of all parts in the ISO 30137 series can be found on the ISO website.

Any feedback or questions on this document should be directed to the user’s national standards body. A complete listing of these bodies can be found at www .iso .org/members .html.

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Introduction

Considerable improvements in the performance of automatic facial recognition (AFR) technologies have resulted in applications such as automated border control using the facial images encoded in e-passports and implemented in systems whereby the identity of a co-operative traveller is verified in an environment designed for the collection of uniformly illuminated and optimally posed images. The success of these first generation AFR systems has encouraged suppliers to consider other applications where the environment for collection of images may be far from optimal. The inferior performance in such less-controlled identification applications may necessitate a greater involvement by trained personnel.

The ISO 30137 series provides guidance on the use of biometric technologies in video surveillance systems (VSS), a framework for performance testing and reporting of such systems, and procedures for establishing ground truth and annotating video data for testing purposes.

This document provides the architecture, use cases and system design. The use cases include real time alerting to the presence of individuals of interest, law enforcement applications such as reviewing post-event video footage from one or more cameras against pre-populated watchlists, commercial uses such as the identification of individuals who are to be given preferential service, and faces added to (enrolled in) a watchlist following observation of behaviours in the video material.

Other scenarios include measurement of crowd densities and determining numbers of individuals traversing a given point. While these are not the focus of this document, they are closely related and information on these is therefore included in Annex A.

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Information technology — Use of biometrics in video surveillance systems —

Part 1: System design and specification

1 Scope

The ISO 30137 series is applicable to the use of biometrics in VSS (also known as Closed Circuit Television or CCTV systems) for a number of scenarios, including real-time operation against watchlists and in post event analysis of video data. In most cases, the biometric mode of choice will be face recognition, but this document also provides guidance for other modalities such as gait recognition.

This document:

— defines the key terms for use in the specification of biometric technologies in a VSS, including metrics for defining performance;

— provides guidance on selection of camera types, placement of cameras, image specification etc. for the operation of a biometric recognition capability in conjunction with a VSS;

— provides guidance on the composition of the gallery (or watchlist) against which facial images from the VSS are compared, including the selection of appropriate images of sufficient quality, and the size of the gallery in relation to performance requirements;

— makes recommendations on data formats for facial images and other relevant information (including metadata) obtained from video footage, used in watchlist images, or from observations made by human operators;

— establishes general principles for supporting the operator of the VSS, including user interfaces and processes to ensure efficient and effective operation, and highlights the need to have suitably trained personnel;

— highlights the need for robust governance processes to provide assurance that the implemented security, privacy and personal data protection measures specific to the use of biometric technologies with a VSS (e.g. internationally recognizable signage) are fit for purpose, and that societal considerations are reflected in the deployed system.

This document also provides information on related recognition and detection tasks in a VSS such as:

— estimation of crowd densities;

— determining patterns of movement of individuals;

— identification of individuals appearing in more than one camera;

— use of other biometric modalities such as gait or iris;

— use of specialized software to infer attributes of individuals, e.g. estimation of gender and age;

— interfaces to other related functionality, e.g. video analytics to measure queue lengths or to alert for abandoned baggage.

INTERNATIONAL STANDARD ISO/IEC 30137-1:2019(E)

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2 Normative references

There are no normative references in this document.

3 Terms and definitions

For the purposes of this document, the following terms and definitions apply.

ISO and IEC maintain terminological databases for use in standardization at the following addresses:

— IEC Electropedia: available at http: //www .electropedia .org/

— ISO Online browsing platform: available at https: //www .iso .org/obp

3.1 Target subject related terms

3.1.1operatorindividual(s) responsible for day to day operation of the system

Note 1 to entry: This may include adjustment of the video surveillance cameras, selecting data suitable for use by the biometric application, and acting on the output of the biometric comparison process.

3.1.2presentation attackpresentation of an artefact or of human characteristics to a biometric capture subsystem in a fashion that could interfere with the intended policy of the biometric system

3.1.3target subject(s)target(s)individual(s) of interest

Note 1 to entry: A target subject will normally be someone already enrolled in a watchlist (3.1.4). However, this is not always the case; in some scenarios they are a target because they are to be enrolled in a watchlist.

3.1.4watchlistlist of individuals of interest (3.1.3) (and their associated reference images) for detection by the video surveillance application

Note 1 to entry: The watchlist may be of individuals for whom an added service level is to be offered (e.g. VIPs or premium customers). This is sometimes referred to as a “whitelist”.

Note 2 to entry: The watchlist may be a list of “wanted” individuals, e.g. individuals who should be denied access to premises or services. This is sometimes referred to as a “blacklist”.

Note 3 to entry: A system may have multiple watchlists of different groups of target subjects, and with different performance goals.

Note 4 to entry: In the case of target subject back-tracking (3.3.1) the watchlist will normally contain only one target subject (3.1.3) (or in the case of a group of individuals of interest, a few target subjects).

3.2 VSS related terms

3.2.1codeccomputer program capable of encoding or decoding a digital data stream or signal

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3.2.2compression ratiomeasure of the compressed file size to that of the uncompressed file size

3.2.3dropped framesframes from the video camera(s) that are not processed or are not available for facial detection and the creation of templates

Note 1 to entry: Normally measured in terms of either the number of frames per second dropped, or the percentage of the frames per second dropped.

3.2.4framesingle image shown as part of a sequence of images in a video stream

3.2.5frame ratefrequency (rate) at which an imaging device produces unique consecutive images called frames (3.2.4)

Note 1 to entry: Frame rate is normally expressed in frames per second (fps).

3.2.6frame sizepixel dimensions of the frame described in terms of horizontal and vertical pixels, and which may also be additionally described in terms of total megapixels

3.2.7post-processingsteps performed after the biometric comparison process

EXAMPLE Triaging decisions based on fusion of quality and score metrics.

3.2.8pre-processingsteps performed prior to the biometric comparison process

EXAMPLE Image quality enhancement, subject detection and feature extraction.

3.2.9resolutionmeasure of the amount of detail that can be stored in an image

Note 1 to entry: Resolution is normally measured in pixels per millimetre.

3.2.10subject trackingprocess of aggregating multiple biometric samples for a single individual, possibly from multiple cameras, to avoid producing separate detection alerts for the same target subject (3.1.3)

3.2.11video management systemVMScomponent of a video surveillance system (3.2.12) that collects video from cameras and other sources, records that video to a storage device and provides an interface to both view the live video and to randomly access recorded video according to time

3.2.12video surveillance systemVSSsystem consisting of camera equipment, monitoring and associated equipment for transmission and controlling purposes, which may be necessary for the surveillance of a protected area




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3.3 Biometric system related terms

3.3.1back-trackingact of finding the given image(s) of a face/individual by searching all video feeds where the individual could have been seen

Note 1 to entry: Back-tracking may or may not use facial biometrics.

3.3.2face detectiondetermination of the presence of faces within a video frame (3.2.4) and production of the location of each face in the frame

Note 1 to entry: Face detection is the first step in the face recognition process.

3.3.3post event analysisnon-realtime analysis of data previously captured by video surveillance cameras

EXAMPLE To identify possible suspects following an incident or event.

3.3.4real time analysison-line processing of video surveillance data as it is captured

EXAMPLE To identify individuals held on a watchlist so that immediate action can be taken.

3.3.5Wiegandde-facto wiring standard commonly used to connect a card swipe mechanism to the rest of an electronic entry system

3.3.6zone of recognition3-dimensional space within the field of view of the camera and in which the imaging conditions for robust biometric recognition are met

Note 1 to entry: In general, the zone of recognition is smaller than the field of view of the camera, e.g. not all faces in the field of view may be in focus and not every face in the field of view is imaged with the necessary inter-eye distance (IED).

3.4 Environment/scenario related terms

3.4.1attractorvisual or acoustic cue within the environment which encourages individuals to look in a particular direction (i.e. towards the camera in a facial recognition application) in an attempt to improve recognition performance

3.4.2choke pointpoint of congestion or obstruction through which individuals pass

3.4.3luxmeasure of illumination intensity




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3.5 Symbols and abbreviated terms

AFIS Automated Fingerprint Identification System

AFR Automated Facial Recognition

CCTV Closed Circuit Television (system), another term for video surveillance (system)

FPS Frames per Second

LFR Live Facial Recognition, real time automated facial recognition using video surveillance cameras

GUI Graphical User Interface

HDR High Dynamic Range

IED Inter Eye Distance, the distance (usually measured in pixels) between the centres of the eyes

IP Internet Protocol

MTF Modulation Transfer Function

NIST National Institute of Standards and Technology

OSDP Open Supervised Device Protocol

PTZ Pan, Tilt and Zoom; a type of video surveillance camera that can be remotely adjusted (man-ually by the operator or automatically by using dedicated software).

SFR Spatial Frequency Response

VMS Video Management System

VSS Video Surveillance System

4 Comparison of terms used in biometric systems with those used in video surveillance

The video surveillance and biometrics communities both have well established vocabularies to describe the various components of a system, but the same term may sometimes be interpreted differently. While the terms listed above apply to this document, Table 1 below highlights some of those terms and expressions where care needs to be taken when communicating with members of the video surveillance community.

Table 1 — Comparison of terms used in biometric systems with those used in video surveillance

Term Definition within the context of automated biometric processing

Definition within the conventional use of human-led VSS, e.g. within the scope

of IEC 62676 seriesCrowd monitor-ing

For example, counting of individuals in a volume, or over a time interval

The observation of a group to determine collective behaviour or as part of a pro-cess to detect anomalous activity

Detection and localization

Biometric detection: the process of finding instances of a particular biometric mode, while correctly rejecting all instances of imagery not representing that biometric mode

Target detection: the process of finding targets of interest, such as humans or cars, in a video feed

Observation Tracking: the process of spatially locating a par-ticular biometric subject as it moves

Target observation: the process of follow-ing a particular target in a video feed




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Term Definition within the context of automated biometric processing

Definition within the conventional use of human-led VSS, e.g. within the scope

of IEC 62676 seriesRecognition The process for assigning a biometric identifier to

a subjectThe process of recognizing a familiar face, synonym for identification

Identification The process of determining a subject’s identity by comparing imagery of a biometric mode against a database formed from imagery of individuals. This generally includes not assigning an identi-fy when the target subject is not present in the database

The process of a human determining a subject’s identity using available (printed) galleries, or use of identity cues (clothing)

Verification The process of confirming a subject’s identity by comparing imagery of a biometric mode against a particular prior sample of a candidate individual

The process of confirming a target’s identity

Inspection Human review of the output from an automat-ed biometric system to assess an alert from the biometric subsystem

Inspection: The detailed review of VSS imagery to determine more detailed in-formation or characteristics, such as age or sex of an individual, brand of clothing, presence of jewellery

Alert An indication that an identifier for an enrolled subject has been returned by the biometric recog-nition process

An indication issued by a camera, opera-tor or system that an event of interest has occurred

5 Architecture

Figure 1 — Components of a biometrically enabled VSS

Table 1 (continued)




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Figure 1 shows the process flow in a typical biometrically enabled VSS with components such as:

1. Video surveillance cameras positioned to collect images in a form which supports comparison with images on the watchlist.

2. A VMS and infrastructure to organise and transmit footage from a number of cameras to the main server and storage system.

3. Software to detect and track faces (and/or other biometric features) in the video stream and to create biometric feature sets in the format developed by the supplier of the biometric recognition system. This can include feature sets created by combining features extracted from multiple face images from a single individual, continuously updated as new video frames are processed.

4. Comparison and decision software, again likely to be proprietary to the supplier of the biometric system, which determines whether the system has recognised an individual on the watchlist. The match criteria and decision thresholds may be different for groups of individuals on the watchlist; e.g. some may be considered low risk, with only minimal implications should they not be recognised by the system, whereas for others it may be imperative that they are recognised as soon as possible.

5. Alerts generated by the automated system are passed to the human operator for assessment.

6. An operator support environment to aid in making decisions on whether an alert should be followed up (and how) or rejected as a false alert.

7. Links to analytics systems to record the event and decisions taken, and to provide access to other information which may assist in disposal of the instance of recognition, e.g. previous instances of a similar match to the individual on the watchlist, and guidance on the appropriate action to be taken.

8. A systems management “bus” which enables configuration and operation of the key components in the biometric recognition system according to threat level, workload of human operators, time of day, etc. and supports the merging of recognitions between cameras across the surveillance domain.

Figure 1 shows an example of a server-centric architecture. However, there are other models available, such as distributed architectures using edge computing (where part of the processing is done in the video camera of the VSS) or where cameras and computing resources are available within smart devices such as smartphones and PCs.

6 Use cases

6.1 General

This section provides examples of some of the different ways in which biometrics can be used in conjunction with VSS to support business needs across a range of organisations, including:

— police and law enforcement (and private security companies, such as those operating shopping malls and car parks) to alert to the presence of individuals of interest;

— police and law enforcement to manage the identification of individuals in video surveillance footage collected after a notable event or incident;

— commercial usage to alert to the presence of individuals of interest for whom special or differentiated levels of service are to be provided;

— commercial or government systems to manage the flow of individuals or queues, e.g. in accordance with agreed service levels;

— border services and client support organizations for quality assurance and customer support, e.g. following a complaint or an incident.

The use cases can be broken down into three broad categories, namely ‘post event’, ‘real-time’ and ‘enrolment’ applications (enrolment may be real time or post event). The following sections provide




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examples of some common use cases, described in terms of performance objectives and the roles played by various components of the system, including the responsibilities of the system operator.

6.2 Post event use cases

In post event use cases the performance objective is the reliable detection, automated feature extraction and searching of large numbers of target subjects against one or more watchlists or databases in an attempt to identify possible suspects, with a high probability that the candidate list returned by the biometric subsystem includes (at a high rank) those target subjects that have a matching template stored in the watchlist.

These use cases are challenging because in many cases the quality and positioning of the video cameras will be beyond the control of the operator of the biometric subsystem, and they will not have been installed with biometric applications in mind.

The operator normally has an “expert” role within the end to end process, selecting images suitable for submission as probes and examining candidates returned following a search of the database. They may be trained in facial comparison techniques, and the decision-making process may be supported by dedicated image analysis tools. In cases such as backtracking or clustering (linking images of the same subjects together) the operator may also make use of other visual information (e.g. the individual’s clothes and relative location of cameras) to help them to confirm or refute potential matches.

Examples of post event use cases include:

— post event analysis of recorded video surveillance material (from one or more cameras) processed with the use of biometric recognition software to identify one or more individuals in frames or sequences (using one or more reference images);

— post event analysis of recorded video surveillance material from more than one camera in which an individual (whether identified or not) is tracked (either forwards or backwards in time) and between cameras. This may involve more than just biometric applications, for example video analytics software;

— retrospective clustering — detecting and extracting faces from multiple sources of video for the purposes of clustering imagery sources of the same individual(s) together. This will normally need to be an automated process due to large numbers of subjects appearing in multiple video streams, although a human operator may subsequently review the results and intervene where they find subjects who have been wrongly classified.

6.3 Real time use cases

In real time applications the performance objective is a high probability of the system alerting for target subjects with a matching template in a watchlist, and a low probability of an alert for subjects not in the watchlist. The watchlist will typically consist of a subset of images drawn from a larger image database, and that have been chosen to address a specific business objective.

These use cases are challenging because of the large amount of data that needs to be processed, especially if the system involves multiple cameras with multiple subjects in each frame. Not only does this present a challenge in terms of search accuracy, it is vital that the end to end response time is fast enough to enable effective action to be taken when an alert is generated.

The role of the operator will typically be to assess any alerts from the biometric subsystem and to make an initial decision as to whether the alert is genuine or if it is a false match. They will usually also be responsible for instigating further action as appropriate, such as directing resources on the ground to detain or speak to the target subject in order to formally confirm their identity.

Examples of real time use cases include:

— alerting in real time (or near real time) to the presence of an individual traversing the field of view of a video surveillance camera, identified by the biometric subsystem as being someone whose




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biometric data (e.g. a facial image) has previously been stored as a reference in a watchlist. For example, checking individuals entering a building or disembarking a plane or train against a watchlist, with the aim of either bestowing or denying particular privileges, and monitoring of video surveillance by law enforcement agencies for the purposes of crime prevention and public safety, This use case is sometimes referred to as Live Facial Recognition (LFR). A practical example illustrating the use of LFR can be found in Annex C;

— real time tracking of a particular individual of interest between the fields of view of a number of cameras, some of which may not overlap.

6.4 Enrolment use cases

In these use cases the goal is the successful enrolment of target subjects of interest into a database or watchlist, such that the biometric templates created are of sufficient quality for the intended use. Prior to enrolment, a biometric search may be carried out to determine if the target subject is already enrolled.

The operator may have a role in selecting the best quality images for enrolment, but in many cases the process will be fully automated. Machine learning and cognitive computing can be applied to help ensure that the best available images are selected for enrolment.

Examples of enrolment use cases include:

— enrolment (into a watchlist) of individuals who enter a protected zone or repeatedly visit the same area;

— “time clocking” individuals in situations where there is an interest in knowing how long they spend in a particular area, for example to monitor the time of service or queue length;

— enrolment of individuals traversing the field of view of a video surveillance camera into a database in order to support a watchlist application for future use within the same system, or to use in conjunction with other biometric applications and databases.

7 Specification of hardware and software

7.1 General

The IEC 62676 series already provides extensive information on camera selection, positioning, network bandwidth, performance considerations, storage requirements etc. for traditional (i.e. non-biometric) applications. This document therefore focuses on those aspects of the hardware and software components of the VSS that have a direct bearing on the performance of the biometric subsystem.

It is important to note that what may be an ideal set up for a conventional VSS may produce images that are very poorly suited for use in a biometric application. While the following recommendations are primarily applicable to an AFR system, they can also be adapted for other modes.

7.2 Physical environment

In many cases the environment in which the VSS is intended to operate will be beyond the control of those responsible for deploying or operating the cameras. Careful positioning of the cameras may help (see 7.5.2), but where it is also possible to exert some influence over the environment where the system operates, the following points should be considered:

— uneven floors and steps should generally be avoided as changes of angle/height often cause individuals to look down, thus making it hard to obtain usable images of the face;

— barriers may be introduced to modify the flow of individuals through the environment, ensuring they all pass through the field of view of the camera(s) at the correct distance and moving towards (in the case of an AFR system) the camera. Such techniques, together with careful positioning of the




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